Cell splitting in neural networks extends strong scaling (Hines et al. 2008)

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Accession:97917
Neuron tree topology equations can be split into two subtrees and solved on different processors with no change in accuracy, stability, or computational effort; communication costs involve only sending and receiving two double precision values by each subtree at each time step. Application of the cell splitting method to two published network models exhibits good runtime scaling on twice as many processors as could be effectively used with whole-cell balancing.
Reference:
1 . Hines M, Eichner H, Schuermann F (2008) Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors J Comput Neurosci 25(1):203-210 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Generic;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Methods;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu];
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splitcell
pardentategyrus
readme.html *
bgka.mod *
CaBK.mod *
ccanl.mod *
Gfluct2.mod *
gskch.mod *
hyperde3.mod *
ichan2.mod *
LcaMig.mod *
nca.mod *
tca.mod *
bg.sh
DG500_M7.hoc *
dgnetactivity.jpg *
dgnettraces.jpg *
init.hoc
initorig.hoc *
modstat *
mosinit_orig.hoc *
out.std
parRI10sp.hoc
RI10sp.hoc
test1.sh *
time *
                            
TITLE Borg-Graham type generic K-A channel
UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)

}

PARAMETER {
	v (mV)
        ek (mV)
	celsius 	(degC)
	gkabar=.01 (mho/cm2)
        vhalfn=-33.6   (mV)
        vhalfl=-83   (mV)
        a0l=0.08      (/ms)
        a0n=0.02    (/ms)
        zetan=-3    (1)
        zetal=4    (1)
        gmn=0.6   (1)
        gml=1   (1)
}


NEURON {
	SUFFIX borgka
	USEION k READ ek WRITE ik
        RANGE gkabar,gka, ik
        GLOBAL ninf,linf,taul,taun
}

STATE {
	n
        l
}

INITIAL {
        rates(v)
        n=ninf
        l=linf
}

ASSIGNED {
	ik (mA/cm2)
        ninf
        linf      
        taul
        taun
        gka
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	gka = gkabar*n*l
	ik = gka*(v-ek)

}


FUNCTION alpn(v(mV)) {
  alpn = exp(1.e-3*zetan*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION betn(v(mV)) {
  betn = exp(1.e-3*zetan*gmn*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION alpl(v(mV)) {
  alpl = exp(1.e-3*zetal*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION betl(v(mV)) {
  betl = exp(1.e-3*zetal*gml*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
}

DERIVATIVE states { 
        rates(v)
        n' = (ninf - n)/taun
        l' = (linf - l)/taul
}

PROCEDURE rates(v (mV)) { :callable from hoc
        LOCAL a,q10
        q10=3^((celsius-30)/10)
        a = alpn(v)
        ninf = 1/(1 + a)
        taun = betn(v)/(q10*a0n*(1+a))
        a = alpl(v)
        linf = 1/(1+ a)
        taul = betl(v)/(q10*a0l*(1 + a))
}


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